data-analyst

Data analysis on a CSV dataset involves loading the file, cleaning it by handling missing values and deduplicating records, and then exploring distributions to detect trends. This workflow streamlines preparation and produces actionable insights with visualizations.

Yes, exploratory data analysis supports JSON and Excel files. The workflow accepts CSV, JSON, Excel, and SQLite inputs, enabling you to load, clean, explore, and analyze these formats to generate structured findings reports with clear visuals.

Visualizing distributions and correlations from a dataset is achieved through exploratory data analysis that generates charts directly from cleaned data. This analyzes distributions and correlations to produce a structured findings report with clear visual explanations.

No external dependencies are required to analyze SQLite data. The Skill handles SQLite inputs natively alongside CSV, JSON, and Excel files, allowing you to load, clean, and explore the database directly without manually installing separate libraries.

Data cleaning for time series analysis works by loading the dataset, handling missing values, deduplicating records, and fixing data types. Once clean, the analysis phase detects time series trends and generates corresponding visual charts for reporting.